Towards scalable estimation of plant functional diversity from Sentinel-2

Autor: Nadejda A. Soudzilovskaia, Nguyen An Binh, Leon T. Hauser, Peter M. van Bodegom, Ângelo Filipe dos Reis Pereira e Cortinhas Sil, J. Timmermans, Jean-Baptiste Féret, Niels van der Windt
Přispěvatelé: Leiden University, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Vietnam Academy of Science and Technology (VAST), Universidade do Porto, Theoretical and Computational Ecology (IBED, FNWI)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
010504 meteorology & atmospheric sciences
Plant functional diversity
Chloropyll content
Computer science
Multispectral
0208 environmental biotechnology
Canopy diversity
Soil Science
02 engineering and technology
01 natural sciences
Biodiversity Plant functional diversity Terrestrial ecosystems Remote sensing Sentinel-2 Multispectral Radiative transfer modeling PROSAIL ARTMO Traitbased Ecology Montesinho Portugal Canopy diversity LMA Leaf mass per area LAI Leaf area index CAB Chloropyll content
Terrestrial ecosystems
LMA
ARTMO
Montesinho
Computers in Earth Sciences
Leaf area index
Traitbased
Independence (probability theory)
ComputingMilieux_MISCELLANEOUS
0105 earth and related environmental sciences
Remote sensing
CAB
Ground truth
Ecology
Portugal
PROSAIL
Sampling (statistics)
Geology
Inversion (meteorology)
Biodiversity
15. Life on land
Radiative transfer modeling
020801 environmental engineering
Leaf mass per area
LAI
[SDE]Environmental Sciences
Trait
Satellite
Sentinel-2
Cartography
Diversity (business)
Zdroj: Remote Sensing of Environment
Remote Sensing of Environment, Elsevier, 2021, 262, pp.112505. ⟨10.1016/j.rse.2021.112505⟩
Remote Sensing of Environment, 262. Elsevier BV
Remote Sensing of the Environment, 262:112505. Elsevier
ISSN: 0034-4257
1879-0704
Popis: Large-scale high-resolution satellite observations of plant functional diversity patterns will greatly benefit our ability to study ecosystem functioning. Here, we demonstrate a potentially scalable approach that uses aggregate plant traits estimated from radiative transfer model (RTM) inversion of Sentinel-2 satellite images to calculate community patterns of plant functional diversity. Trait retrieval relied on simulations and Look-up Tables (LUTs) generated by a RTM rather than heavily depending on a priori field data and data-driven statistical learning. This independence from in-situ training data benefits its scalability as relevant field data remains scarce and difficult to acquire. We ran a total of three different inversion algorithms that are representative of commonly applied approaches and we used two different metrics to calculate functional diversity. In tandem with Sentinel-2 image-based estimation of plant traits, we measured Leaf Area Index (LAI), leaf Chlorophyll content (CAB), and Leaf Mass per Area (LMA) in-situ in a (semi-)natural heterogeneous landscape (Montesinho region) located in northern Portugal. Sampling plots were scaled and georeferenced to match the satellite observed pixels and thereby allowed for a direct one-to-one posterior ground truth validation of individual traits and functional diversity. Across approaches, we observe a reasonable correspondence between the satellite-based retrievals and the insitu observations in terms of the relative distribution of individual trait means and plant functional diversity across locations despite the heterogeneity of the landscape and canopies. The functional diversity estimates, based on a combination of canopy and leaf traits, were robust against estimation biases in trait means. Particularly, the convex hull volume estimate of functional diversity showed strong concordance with in-situ observations across all three inversion methods (Spearman's rho: 0.67-0.80). The remotely sensed estimates of functional diversity also related to in-situ taxonomic diversity (Spearman's rho: 0.55-0.63). Our work highlights the potential and challenges of RTM-based functional diversity metrics to study spatial community-level ecological patterns using currently operational and publicly available Sentinel-2 imagery. While further validation and assessment across different ecosystems and larger datasets are needed, the study contributes towards a further maturation of scalable, spatially, and temporally explicit methods for functional diversity assessments from space. The authors would like to acknowledge Christian Rossi, Prof. Geof-frey M. Henebry, and the anonymous reviewers for their valuable comments and suggestions that greatly improved the manuscript. This work was supported financially by the Ecology Fund of the RoyalNetherlands Academy of Arts and Sciences (‘KNAW Fonds Ecologie’; KNAWWF/807/19011). We thank Altino Geraldes, Joao Carlos Aze-vedo, and the local farmers and foresters in the Montesinho-Nogueira Natura 2000 site for their help and collaboration. We thank Emilie Didaskalou for her lab assistance. J.-B. F ́eret acknowledges financial support from Agence Nationale de la Recherche (BioCop project—ANR- 17-32CE-0001).
Databáze: OpenAIRE
Externí odkaz: